Fixed-wing unmanned aerial vehicle heavy landing state monitoring method, electronic device and medium

Abstract

The application discloses a fixed-wing unmanned aerial vehicle heavy landing state monitoring method, an electronic device and a medium. The method can comprise: judging the unmanned aerial vehicle grounding state according to the wheel speed, determining a heavy landing state judgment time; collecting unmanned aerial vehicle data within the heavy landing state judgment time, calculating the sinking rate of the unmanned aerial vehicle main landing gear; calculating the compression stroke of the unmanned aerial vehicle main landing gear strut sleeve through the sinking rate and the unmanned aerial vehicle data; judging whether the unmanned aerial vehicle has heavy landing according to the compression stroke of the unmanned aerial vehicle main landing gear strut sleeve. The application judges whether the unmanned aerial vehicle has heavy landing by taking the compression stroke of the main landing gear strut sleeve as an index, has higher prediction accuracy, and realizes accurate judgment of the heavy landing state under a lower sampling frequency.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) flight safety and control, and more specifically, to a method, electronic device and medium for monitoring the hard landing status of a fixed-wing UAV. Background Technology

[0002] The traditional method for monitoring the landing status of drones is to monitor the drone's altitude and velocities upon touchdown, calculate the impact force absorbed by the landing gear and fuselage structure, and then determine whether the landing load exceeds the structural strength of the drone and the landing gear itself. However, the measurement of the drone's altitude and velocities at the moment of touchdown is prone to errors, leading to misjudgments by pilots and causing accidents such as tire blowouts.

[0003] Meanwhile, the climb rate data is monitored using a high-sampling-frequency sensor. This high-sampling-frequency sensor receives a large amount of data, consuming significant chip memory and increasing the CPU's computational load. Conversely, with low sensor sampling frequencies, climb rate monitoring methods often fail to determine whether a drone has undergone a hard landing.

[0004] Furthermore, current monitoring methods mostly rely on pilots observing data from ground station flight monitoring software to determine the landing status of drones, which cannot achieve intelligent and autonomous judgment, increasing labor costs and the risk of misjudgment.

[0005] Therefore, it is necessary to develop a method, electronic equipment, and medium for monitoring the heavy landing status of fixed-wing UAVs.

[0006] The information disclosed in the background section of this invention is intended only to enhance the understanding of the general background of this invention and should not be construed as an admission or in any way implying that such information constitutes prior art known to those skilled in the art. Summary of the Invention

[0007] This invention proposes a method, electronic device, and medium for monitoring the hard landing status of a fixed-wing unmanned aerial vehicle (UAV). It can use the compression stroke of the main landing gear strut sleeve as an indicator to determine whether the UAV has made a hard landing, with higher prediction accuracy and accurate judgment of the hard landing status at a lower sampling frequency.

[0008] In a first aspect, embodiments of this disclosure provide a method for monitoring the hard landing status of a fixed-wing unmanned aerial vehicle (UAV), including: Determine the drone's grounding status based on wheel speed to determine the timing for a hard landing; Data from the UAV is collected during the heavy landing condition judgment time, and the sinking rate at the main landing gear of the UAV is calculated. The compression stroke of the main landing gear strut sleeve of the UAV is calculated using the sinking rate and the UAV data. Determine whether the drone has experienced a hard landing by measuring the compression stroke of the main landing gear strut sleeve.

[0009] Preferably, when the wheel speed is greater than 0.9 times the ground speed, the drone is judged to be in a grounded state.

[0010] Preferably, the landing status is determined at 1 second before touchdown, 1 second after touchdown, and 3 seconds after touchdown.

[0011] Preferably, the UAV data includes the vertical velocity at the UAV inertial navigation system, the UAV pitch angle, the UAV pitch rate of change, the UAV roll angle, the UAV vertical acceleration, the UAV lateral acceleration, and the distance between the UAV inertial navigation system and the main landing gear strut sleeve.

[0012] Preferably, the sinking rate of the main landing gear of the UAV is:

[0013] in, The vertical velocity at the main landing gear, i.e., the sink rate. The vertical velocity at the inertial navigation system is... The relative velocity of the main landing gear with respect to the inertial navigation system. Let be the pitch rate of the aircraft. This refers to the distance between the UAV's inertial navigation system and the main landing gear strut sleeve. The angle between the line connecting the UAV inertial navigation system and the main landing gear strut sleeve and the horizontal line.

[0014] Preferably, the compression stroke of the UAV main landing gear strut sleeve is:

[0015] in, The compression stroke of the main landing gear sleeve. The sampling period is , The sensor sampling frequency, The vertical acceleration of the main landing gear, This represents the number of samples taken at the sensor's sampling frequency.

[0016] Preferably, it further includes: After determining whether the drone has made a hard landing, corresponding landing tags are affixed to the drone data for both normal and hard landings. A support vector machine model is established and trained using UAV data corresponding to different landing labels. The trained model can automatically identify whether a large fixed-wing drone has made a hard landing.

[0017] Preferably, the input of the model is the UAV data corresponding to different landing labels, and the output is the landing status.

[0018] Secondly, embodiments of this disclosure also provide an electronic device, the electronic device comprising: Memory, which stores executable instructions; A processor that executes the executable instructions in the memory to implement the fixed-wing UAV hard landing status monitoring method.

[0019] Thirdly, this disclosure also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the fixed-wing UAV hard landing status monitoring method.

[0020] Its beneficial effects are as follows: 1. This invention proposes a method for calculating the sinking rate at the main landing gear, providing a method for calculating the vertical velocity at the main landing gear; 2. This invention proposes a method for calculating the compression stroke of the main landing gear strut sleeve, and proposes to use the compression stroke of the main landing gear strut sleeve as an indicator to determine whether the UAV has made a hard landing. Compared with existing methods, the prediction accuracy is higher, and accurate judgment of the hard landing state is achieved at a lower sampling frequency. 3. The SVM model method in this invention has the advantage of intelligently and autonomously determining whether a UAV has experienced a hard landing.

[0021] The methods and apparatus of the present invention have other features and advantages that will be apparent from or will be set forth in detail in the accompanying drawings and following detailed description, which together serve to explain the particular principles of the invention. Attached Figure Description

[0022] The above and other objects, features and advantages of the present invention will become more apparent from the more detailed description of exemplary embodiments of the invention in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same parts.

[0023] Figure 1 A flowchart illustrating the steps of a fixed-wing unmanned aerial vehicle (UAV) hard landing status monitoring method according to an embodiment of the present invention is shown.

[0024] Figure 2 A motion analysis diagram for calculating the main landing gear sink rate according to an embodiment of the present invention is shown. Detailed Implementation

[0025] Preferred embodiments of the invention will now be described in more detail. While preferred embodiments of the invention are described below, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0026] Figure 1 A flowchart illustrating the steps of a fixed-wing unmanned aerial vehicle (UAV) hard landing status monitoring method according to an embodiment of the present invention is shown.

[0027] like Figure 1 As shown, the fixed-wing UAV hard landing status monitoring method includes: Step 101, determining the UAV's grounding status based on wheel speed and determining the hard landing status judgment time; Step 102, collecting UAV data within the hard landing status judgment time and calculating the sinking rate at the UAV's main landing gear; Step 103, calculating the compression stroke of the UAV's main landing gear strut sleeve based on the sinking rate and UAV data; Step 104, determining whether the UAV has experienced a hard landing based on the compression stroke of the UAV's main landing gear strut sleeve.

[0028] In one example, the drone is considered to be in a ground-based state when the wheel speed is greater than 0.9 times the ground speed.

[0029] In one example, the re-landing status is determined 1 second before touchdown, 1 second after touchdown, and 3 seconds after touchdown.

[0030] In one example, the UAV data includes the vertical velocity at the UAV's inertial navigation system, the UAV's pitch angle, the UAV's pitch rate of change, the UAV's roll angle, the UAV's vertical acceleration, the UAV's lateral acceleration, and the distance between the UAV's inertial navigation system and the main landing gear strut sleeve.

[0031] In one example, the sinking rate at the main landing gear of the drone is:

[0032] in, The vertical velocity at the main landing gear, i.e., the sink rate. The vertical velocity at the inertial navigation system is... The relative velocity of the main landing gear with respect to the inertial navigation system. Let be the pitch rate of the aircraft. This refers to the distance between the UAV's inertial navigation system and the main landing gear strut sleeve. The angle between the line connecting the UAV inertial navigation system and the main landing gear strut sleeve and the horizontal line.

[0033] In one example, the compression stroke of the drone's main landing gear strut sleeve is:

[0034] in, The compression stroke of the main landing gear sleeve. The sampling period is , The sensor sampling frequency, The vertical acceleration of the main landing gear, This represents the number of samples taken at the sensor's sampling frequency.

[0035] In one example, it also includes: After determining whether the drone has made a hard landing, corresponding landing tags are affixed to the drone data for both normal and hard landings. Establish a support vector machine model and train the model using UAV data corresponding to different landing labels; The trained model can automatically identify whether a large fixed-wing drone has made a hard landing.

[0036] In one example, the model takes drone data corresponding to different landing labels as input and outputs the landing status.

[0037] Specifically, the touchdown moment of the main landing gear is determined based on the drone's wheel speed being greater than 0.9 times the ground speed for 1 second. Sensor data for a total of 5 seconds are selected: 1 second before touchdown, 1 second after touchdown, and 3 seconds after touchdown. This includes the drone's vertical / horizontal velocity at the inertial navigation system (INS), pitch angle, rate of pitch change, roll angle, vertical acceleration, and lateral acceleration. The distance between the drone's INS and the main landing gear strut sleeve is also measured.

[0038] Figure 2 A motion analysis diagram for calculating the main landing gear sink rate according to an embodiment of the present invention is shown.

[0039] like Figure 2 As shown, the motion of the UAV at the moment of landing is regarded as the planar motion of a rigid body. Using the vertical velocity at the UAV's inertial navigation system, the UAV's pitch angle, the UAV's pitch rate of change, and the distance between the UAV's inertial navigation system and the main landing gear strut sleeve, the vertical velocity at the main landing gear is obtained using the base point method, which is the UAV's main landing gear sink rate.

[0040] In the formula, The vertical velocity at the main landing gear, i.e., the sink rate. The vertical velocity at the inertial navigation system is... The relative velocity of the main landing gear with respect to the inertial navigation system. Let be the pitch rate of the aircraft. This refers to the distance between the UAV's inertial navigation system and the main landing gear strut sleeve. The angle between the line connecting the UAV inertial navigation system and the main landing gear strut sleeve and the horizontal line.

[0041] Treating the motion of the main landing gear strut sleeve during UAV landing as uniform acceleration / deceleration, the compression stroke of the main landing gear sleeve within 5 seconds is calculated using the UAV sink rate, the sampling frequency of the inertial navigation system, the number of samples at the current sampling frequency, and the vertical acceleration of the main landing gear:

[0042] In the formula, The compression stroke of the main landing gear strut sleeve. The sampling period is , The sensor sampling frequency, The vertical acceleration of the main landing gear, This represents the number of samples taken at the sensor's sampling frequency.

[0043] The method of determining whether a large fixed-wing UAV has made a hard landing by measuring the compression stroke of the main landing gear sleeve during each landing is significantly different from the traditional method of judging by the takeoff and landing speed, even at low sampling frequencies.

[0044] Sensor data from normal and hard landings are labeled with landing tags. A support vector machine (SVM) model is built using sink rate, lateral acceleration, roll angle, vertical acceleration, pitch rate, main landing gear strut sleeve compression stroke, and landing tags. The SVM model is then used to automatically identify whether a large fixed-wing UAV has experienced a hard landing.

[0045] Building an SVM model involves loading data, splitting the training and test sets, constructing the SVM model, and evaluating the model using a confusion matrix.

[0046] The SVM model is built using the following code: from sklearn import svm from sklearn.model_selection import train_test_split from sklearn.metrics import confusion_matrix from sklearn.metrics import accuracy_score import pandas as pd df=pd.read_csv("UAVhardLandinfg.csv") zzldata=df.to_numpy() zzlfeature=zzldata[:,0:5] zzllabel=zzldata[:,6] train_data,test_data=train_test_split(zzlfeature,random_state=1,train_size=0.7,test_size=0.3) train_label,test_label=train_test_split(zzllabel,random_state=1,train_size=0.7,test_size=0.3) classifier=svm.SVC(C=2,kernel="rbf",gamma=10,decision_function_shape="ovr") classifier.fit(train_data,train_label.ravel()) pre_train=classifier.predict(train_data) pre_test=classifier.predict(test_data) print("train:",accuracy_score(train_label,pre_train)) print("test:",accuracy_score(test_label,pre_test)) cm=confusion_matrix(test_label,pre_test) print(cm) The present invention also provides an electronic device, comprising: a memory storing executable instructions; and a processor executing the executable instructions in the memory to implement the above-described method for monitoring the hard landing status of a fixed-wing unmanned aerial vehicle.

[0047] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for monitoring the hard landing status of a fixed-wing unmanned aerial vehicle.

[0048] To facilitate understanding of the solutions and effects of the embodiments of the present invention, three specific application examples are given below. Those skilled in the art should understand that these examples are merely for the purpose of understanding the present invention, and any specific details therein are not intended to limit the present invention in any way.

[0049] Example 1

[0050] 1. Select the time from 1 second before aircraft touchdown to touchdown (WHEEL_SPD>0.9) The 3-second phase following GROUND_SPD.

[0051] 2. Select the required flight monitoring parameters

[0052] PITCH_RATE, PITCH,VERTI_ACC, WHEEL_SPD, GROUND_SPD, LAT_ACC, ROLL,VERTICAL_SPD.

[0053] 3. Convert pitch angle to radians: (PITCH / 180) np.pi and PITCHL are high-frequency parameters, processed according to telemetry frequencies. Conversion is available as needed, and functions can be used for direct calculation.

[0054] 4. Convert the pitch angle change rate in ° / s to radians / s: (PITCH_RATE / 180) np.pi and PITCH_RATE are high-frequency parameters, processed according to telemetry frequency. Conversion is available as needed, and functions can be used for direct calculation.

[0055] 5. The wheel speed collected by the brake sensor is a high-frequency parameter and is processed according to the telemetry frequency; the ground speed is a high-frequency parameter and is also processed according to the telemetry frequency.

[0056] 6. Calculate the new parameter, the subsidence rate v. LG = VERTICAL_SPD - (PITCH_RATE / 180) np.pi) np.cos((PITCH / 180) np.pi).

[0057] 7. Calculate the newly added parameter compression stroke (the distance moved within 5 seconds), deta = v LG T + 0.5 VERTI_ACC T 2, .

[0058] 8. Extract the time from 1 second before aircraft touchdown to grounding (WHEEL_SPD>0.9) In the 3-second phase following GROUND_SPD, the four parameters LAT_ACC, ROLL, VERTI_ACC, and PITCH_RATE are all processed according to the telemetry frequency.

[0059] 9. Extract the time from 1 second before aircraft touchdown to grounding (WHEEL_SPD>0.9) The maximum value of the VERTICAL_SPD parameter in the 3-second phase after GROUND_SPD (processed according to telemetry frequency, take the maximum value).

[0060] 10. Extract the time from 1 second before aircraft touchdown to grounding (WHEEL_SPD>0.9) In the 3-second phase following GROUND_SPD, the column of the newly added parameter deta is summed.

[0061] 11. Output 8 metrics.

[0062] 12. Extract the following data from 100 UAV landing times: vertical speed (VERTICAL_SPD), lateral acceleration (LAT_ACC), roll angle (ROLL), vertical acceleration (VERTI_ACC), pitch rate (PITCH_RATE), and main landing gear strut sleeve compression stroke. The training set and test set are divided into training set and test set, and an SVM model is built to automatically determine the landing label data.

[0063] 13. Experimental Analysis

[0064] 1) Comparison of monitoring methods at high sampling frequencies

[0065] a) Calculation of normal landing data when the inertial navigation sampling frequency is high. Data for 5 seconds, from 1 second before main wheel touchdown to 3 seconds after touchdown, is extracted. The monitoring data extracted from the touchdown rules is shown in Table 1.

[0066] Table 1 Monitoring data extracted from the ground contact rule

[0067] The main landing gear settling ratio was calculated. ), main landing gear sleeve compression stroke within a single sampling time ( As shown in Table 2.

[0068] Table 2. Sinking rate and main landing gear sleeve compression stroke within a single sampling time.

[0069] Sum the main landing gear sleeve compression stroke within a single sampling time in Table 2 ( The compression stroke of the main landing gear sleeve within the design time of 5 seconds was found to be -0.0045.

[0070] b) When the inertial navigation sampling frequency is high, the hard landing data calculation extracts data from 1 second before the main wheel touches the ground and 3 seconds after touchdown, for a total of 5 seconds. The monitoring data extracted from the touchdown rules are shown in Table 3.

[0071] Table 3 Monitoring data extracted from the ground contact rule

[0072] The main landing gear settling rate was calculated. ), main landing gear sleeve compression stroke within a single sampling time ( As shown in Table 4.

[0073] Table 4. Sinking rate and main landing gear sleeve compression stroke within a single sampling time.

[0074] Sum the main landing gear sleeve compression stroke within a single sampling time in Table 4 ( The compression stroke of the main landing gear sleeve within the design time of 5 seconds was -2.3583.

[0075] c) Analysis of test results. The test results show that, at high sampling frequencies, the main landing gear sleeve compression stroke during a hard landing is 524 times that of a normal landing compared to a hard landing. Furthermore, the landing speed during a hard landing of the UAV is 18 times that of a normal landing. This indicates that the difference between a hard landing and a normal landing is greater with the method described in this patent, demonstrating higher accuracy and lower risk of misjudgment.

[0076] 2) Comparison of monitoring methods at low sampling frequencies

[0077] Statistical data shows that within the designed 5-second main landing gear sleeve compression stroke, a value close to zero (a negative) or higher indicates a normal UAV landing; a value below 'a' indicates a hard landing. Table 5 presents hard landing data for UAVs at low sampling frequencies. At low sampling frequencies, monitoring sensor data and sinking rate (…) are used to determine… ) and the main landing gear sleeve compression stroke within a single sampling time ( The calculation results are shown in Table 6. The compression stroke of the main landing gear sleeve within the design time of 5 seconds is obtained. The value is -0.40355, which is far less than 0 and not above the 'a' value which is close to 0, indicating a hard landing. However, the landing speed at touchdown is insufficient to determine whether a hard landing occurred. Therefore, this method effectively identifies hard landings of drones at low sampling frequencies.

[0078] Table 5. Heavy landing data at low sampling frequency

[0079] Table 6. Sinking Rate and Main Landing Gear Sleeve Compression Stroke in a Single Sampling Time

[0080] An intelligent SVM model was established using the SVM model input variables in Table 7 and the landing tag data. Then, an autonomous judgment test for UAV re-landing was conducted using the current monitoring data of the UAV. Based on the judgment results, a confusion matrix was output. The confusion matrix showed that the accuracy of the model was over 90%.

[0081] Table 7 Input variables of the SVM model

[0082] This invention can monitor the landing status of UAVs at low sensor sampling frequencies, and has the advantages of low monitoring cost, high intelligence, and wide applicability.

[0083] Example 2

[0084] This disclosure provides an electronic device, comprising: a memory storing executable instructions; and a processor executing the executable instructions in the memory to implement the aforementioned fixed-wing unmanned aerial vehicle (UAV) hard landing status monitoring method.

[0085] An electronic device according to an embodiment of the present disclosure includes a memory and a processor.

[0086] This memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0087] The processor may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of this disclosure, the processor is used to execute computer-readable instructions stored in the memory.

[0088] Those skilled in the art will understand that, in order to solve the technical problem of how to achieve a good user experience, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included within the protection scope of this disclosure.

[0089] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0090] Example 3

[0091] This disclosure provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the fixed-wing unmanned aerial vehicle (UAV) hard landing status monitoring method.

[0092] A computer-readable storage medium according to embodiments of the present disclosure stores non-transitory computer-readable instructions. When these non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the methods described in the foregoing embodiments of the present disclosure are performed.

[0093] The aforementioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or portable hard drive), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).

[0094] Those skilled in the art should understand that the above description of the embodiments of the present invention is only intended to illustrate the beneficial effects of the embodiments of the present invention, and is not intended to limit the embodiments of the present invention to any of the examples given.

[0095] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A method for monitoring the hard landing status of a fixed-wing unmanned aerial vehicle (UAV), characterized in that, include: Determine the drone's grounding status based on wheel speed to determine the timing for a hard landing; Data from the UAV is collected during the heavy landing condition judgment time, and the sinking rate at the main landing gear of the UAV is calculated. The compression stroke of the main landing gear strut sleeve of the UAV is calculated using the sinking rate and the UAV data. Determine whether the drone has experienced a hard landing based on the compression stroke of the main landing gear strut sleeve; The sinking rate of the main landing gear of the drone is as follows: in, The vertical velocity at the main landing gear, i.e., the sink rate. The vertical velocity at the inertial navigation system is... Let be the pitch rate of the aircraft. This refers to the distance between the UAV's inertial navigation system and the main landing gear strut sleeve. The angle between the line connecting the UAV inertial navigation system and the main landing gear strut sleeve and the horizontal line; The compression stroke of the main landing gear strut sleeve of the UAV is as follows: in, The compression stroke of the main landing gear sleeve The sampling period is , The sensor sampling frequency, The vertical acceleration of the main landing gear, This represents the number of samples taken at the sensor's sampling frequency.

2. The method for monitoring the hard landing status of a fixed-wing unmanned aerial vehicle according to claim 1, wherein, When the wheel speed is greater than 0.9 times the ground speed, the drone is considered to be in a grounded state.

3. The method for monitoring the hard landing status of a fixed-wing unmanned aerial vehicle according to claim 1, wherein, The landing status is determined at 1 second before touchdown, 1 second after touchdown, and 3 seconds after touchdown.

4. The method for monitoring the hard landing status of a fixed-wing unmanned aerial vehicle according to claim 1, wherein, The UAV data includes the vertical velocity at the UAV inertial navigation system, the UAV pitch angle, the UAV pitch rate of change, the UAV roll angle, the UAV vertical acceleration, the UAV lateral acceleration, and the distance between the UAV inertial navigation system and the main landing gear strut sleeve.

5. The method for monitoring the hard landing status of a fixed-wing unmanned aerial vehicle according to claim 1, wherein, Also includes: After determining whether the drone has made a hard landing, corresponding landing tags are affixed to the drone data for both normal and hard landings. A support vector machine model is established and trained using UAV data corresponding to different landing labels. The trained model can automatically identify whether a large fixed-wing drone has made a hard landing.

6. The method for monitoring the hard landing status of a fixed-wing unmanned aerial vehicle according to claim 5, wherein, The model takes as input the UAV data corresponding to different landing labels and outputs the landing status.

7. An electronic device, characterized in that, The electronic device includes: Memory, which stores executable instructions; A processor that executes the executable instructions in the memory to implement the fixed-wing unmanned aerial vehicle (UAV) hard landing status monitoring method according to any one of claims 1-6.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the fixed-wing unmanned aerial vehicle (UAV) hard landing status monitoring method according to any one of claims 1-6.

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